Thoughtful elderly monitoring in a smart home environment

ABSTRACT

Various arrangements are presented for monitoring a resident of a residence. A confidence assessment may be performed based on a plurality of smart home devices in the residence. The residence may be identified as eligible for monitoring of the resident based on the confidence assessment. A learning process may be performed to create an ordinary behavior model. Data that is received from the plurality of smart home devices may be monitored to identify data indicative of behavior considered unusual based on the ordinary behavior model. An alert may be created that identifies the behavior and identifies how the behavior contrasts with the ordinary behavior model.

BACKGROUND

Frequently, adult children live a significant distance from theirparents. As the parents age, keeping track of a parent's well-being isoften a task that falls to adult children, especially if the parentlives alone. Conventionally, this task is accomplished by frequenttelephone calls and emails. Such an arrangement can tend to lead tocheck-ins with the parent only when convenient to the adult child,check-ins occurring at random times, and/or significant time gapsoccurring between check-ins due to when the adult child remembers tocheck in. Further, such an arrangement may not allow the adult child totruly understand if the parent is in need of help. For instance, theparent may be well enough to conduct a phone call, but his or herbehavior off the phone may be erratic, possibly indicative of a physicalor mental condition.

SUMMARY

Various embodiments are described related to a method for monitoring aresident. In some embodiments, a method for monitoring a resident isdescribed. The method may include performing a confidence assessmentbased on a plurality of smart home devices being present within aresidence linked with a user account. The resident may reside at theresidence. The method may include determining whether the residence iseligible for monitoring of the resident based on the confidenceassessment. The method may include performing a learning process over aperiod of time during which resident activity data is collected from theplurality of smart home devices and analyzed to create an ordinarybehavior model. The method may include following determining that theresidence is eligible for monitoring of the resident and the learningprocess being performed, providing a notification that monitoring isactive. The method may include monitoring data received from theplurality of smart home devices to identify data indicative of behaviorconsidered unusual based on the ordinary behavior model. The method mayinclude creating an alert that identifies the behavior and identifieshow the behavior contrasts with the ordinary behavior model. The methodmay include sending, to an administrator device linked with the useraccount, the alert that identifies the behavior and identifies how thebehavior contrasts with the ordinary behavior model.

Embodiments of such a method may include one or more of the followingfeatures: The method may include performing the confidence assessment.Performing the confidence assessment may include identifying a number ofthe plurality of smart home devices that are eligible to participate inthe monitoring. Performing the confidence assessment may includeidentifying a second number of the plurality of smart home devices thatare eligible to participate in the monitoring and are power-constraineddevices. The method may include, in response to the monitoring beingactivated, activating a process at each exclusively battery-poweredsmart home device of the plurality of smart home devices that definesone or more rules indicative of when data indicative of a behavior ofthe resident should be stored for periodic scheduled transmission to amonitoring server system or the data indicative of the behavior of theresident should be transmitted immediately to the monitoring serversystem. Performing the confidence assessment may include providing aquestionnaire to the administrator device linked with the user account.The questionnaire may require that a user of the administrator deviceidentify a specific location of each smart home device of the pluralityof smart home devices within the residence. The questionnaire mayrequire that a user of the administrator device identify a plurality oftypes of worrisome scenarios of which the user of the administratordevice desires to be notified. Performing the confidence assessment mayinclude providing the questionnaire to the administrator device linkedwith the user account. The questionnaire may require that a user of theadministrator device provide an indication of a number of residents thatlive in the residence. The questionnaire may require that a user of theadministrator device provide an indication that no cats or dogs livewith the resident. Performing the confidence assessment may includecalculating a confidence metric based on the number of the plurality ofsmart home devices that are eligible to participate in the monitoring.Performing the confidence assessment may include calculating aconfidence metric based on the second number of the plurality of smarthome devices that are eligible to participate in the monitoring and arepower-constrained. Performing the confidence assessment may includecalculating a confidence metric based on the responses to thequestionnaire received from the administrator device. Performing theconfidence assessment may include calculating a confidence metric basedon comparing the calculated confidence metric to a confidence metricthreshold. The plurality of smart home devices may be selected from thegroup consisting of: a smart home smoke detector; a smart home carbonmonoxide detector; a smart indoor security camera; a smart outdoorsecurity camera; a smart thermostat; a smart home assistant device; asmart security system; a smart window/door sensor; a smartphone; and asmart doorbell device. The plurality of smart home devices may includeeither a video camera, microphone, or a motion sensor. The method mayinclude outputting, via a smart home device of the plurality of smarthome devices, the alert.

In some embodiments, the method may include monitoring the residentusing the plurality of smart home devices over a trial period of time togenerate a body of trial monitoring data, the trial period of time beingsufficient to encompass at least a plurality of daily- and/orday-of-week-specific activity routines of the resident. The method mayinclude processing the trial monitoring data to determine whether theplurality of smart home devices are sufficient in type, number, andlocation to sufficiently track the resident through their daily- and/orday-of-week-specific activity routines according to a predeterminedthreshold criterion. The method may include determining that theplurality of smart home devices are not sufficient in at least one oftype, number, and location to sufficiently track the resident accordingto the predetermined threshold criterion, and, responsive to saiddetermining, sending to the administrator device a notification that theresidence is not eligible for said monitoring of the resident. Thepredetermined threshold criterion may include that, for at least athreshold percentage of each day of the trial period, a location of theresident within the residence is identifiable solely using the trialmonitoring data. The threshold percentage may be at least 95 percent.The predetermined threshold criterion may include that, for at least thethreshold percentage of each day of the trial period, a breathing rateor heartbeat rate of the resident within the residence is identifiablesolely using the trial monitoring data. The trial period of time may bea default value of at least seven days, and wherein said performing theconfidence assessment is carried out without requiring user input. Thetrial period of time may be received from a user via the administrativedevice.

In some embodiments, a system for monitoring a resident is described.The system may include a plurality of smart home devices installedwithin a residence linked with a user account. The system may include anapplication executed by an administrator device. The system may includea cloud-based host system. The cloud-based host system may include oneor more processors. The cloud-based host system may include a memorycommunicatively coupled with and readable by the one or more processors.The cloud-based host system, having stored therein processor-readableinstructions which, when executed by the one or more processors, maycause the one or more processors to perform a confidence assessmentbased on the plurality of smart home devices being present within theresidence linked with the user account. The resident may reside at theresidence. The one or more processors may determine whether theresidence is eligible for monitoring of the resident based on theconfidence assessment. The one or more processors may perform a learningprocess over a period of time during which resident activity data iscollected from the plurality of smart home devices and analyzed tocreate an ordinary behavior model. The one or more processors, followingdetermining that the residence is eligible for monitoring of theresident and the learning process being performed, may provide anotification that monitoring is active. The one or more processors maymonitor data received from the plurality of smart home devices toidentify data indicative of behavior considered unusual based on theordinary behavior model. The one or more processors may create an alertthat identifies the behavior and identifies how the behavior contrastswith the ordinary behavior model. The one or more processors may send,to the administrator device linked with the user account, the alert thatidentifies the behavior and identifies how the behavior contrasts withthe ordinary behavior model. The processor-readable instructions thatcause the one or more processors to perform the confidence assessmentinclude processor-readable instructions that may cause the one or moreprocessors to identify a number of the plurality of smart home devicesthat are eligible to participate in the monitoring. Theprocessor-readable instructions that cause the one or more processors toperform the confidence assessment include processor-readableinstructions that may cause the one or more processors to identify asecond number of the plurality of smart home devices that are eligibleto participate in the monitoring and are power-constrained devices.

Additionally or alternatively, embodiments of such a system may includeone or more of the following features: The processor-readableinstructions may be configured to cause the one or more processors to,in response to the monitoring being activated, activate a process ateach power-constrained smart home device of the plurality of smart homedevices that defines one or more rules indicative of when dataindicative of a behavior of the resident should be stored for periodicscheduled transmission to a monitoring server system or the dataindicative of the behavior of the resident should be transmittedimmediately to the monitoring server system. The processor-readableinstructions that cause the one or more processors to perform theconfidence assessment include processor-readable instructions that maycause the one or more processors to provide a questionnaire to theadministrator device linked with the user account. The questionnaire mayrequire that a user of the administrator device identify a specificlocation of each smart home device of the plurality of smart homedevices with the residence. The questionnaire may require that a user ofthe administrator device identify a plurality of types of worrisomescenarios of which the administrator user desires to be notified. Theprocessor-readable instructions that cause the one or more processors toperform the confidence assessment may include processor-readableinstructions that cause the one or more processors to provide thequestionnaire to the administrator device linked with the user account.The questionnaire may require that a user of the administrator deviceprovide an indication of a number of residents that live in theresidence. The questionnaire may require that a user of theadministrator device provide an indication that no cats or dogs livewith the resident. The processor-readable instructions that cause theone or more processors to perform the confidence assessment includeprocessor-readable instructions that may cause the one or moreprocessors to calculate a confidence metric based on the number of theplurality of smart home devices that are eligible to participate in themonitoring. The processor-readable instructions may cause the one ormore processors to calculate a confidence metric based on the secondnumber of the plurality of the smart home devices that are eligible toparticipate in the monitoring and are power-constrained. Theprocessor-readable instructions may cause the one or more processors tocalculate a confidence metric based on the responses to thequestionnaire received from the administrator device. Theprocessor-readable instructions may cause the one or more processors tocalculate a confidence metric based on comparing the calculatedconfidence metric to a confidence metric threshold. The plurality ofsmart home devices are selected from the group consisting of: a smarthome smoke detector; a smart home carbon monoxide detector; a smartindoor security camera; a smart outdoor security camera; a smartthermostat; a smart home assistant device; a smart security system; asmart window/door sensor; a smartphone; and a smart doorbell device. Thesystem may output the alert.

In some embodiments, a non-transitory processor-readable mediumcomprising processor-readable instructions is described. Theprocessor-readable instructions may cause the one or more processors toperform a confidence assessment based on a plurality of smart homedevices being present within a residence linked with a user account. Theresident may reside at the residence. The one or more processors may becaused by the instructions to determine whether the residence iseligible for monitoring of the resident based on the confidenceassessment. The one or more processors may be caused by the instructionstop perform a learning process over a period of time during whichresident activity data is collected from the plurality of smart homedevices and analyzed to create an ordinary behavior model. The one ormore processors, following determining that the residence is eligiblefor monitoring of the resident and the learning process being performed,may be caused by the instructions to provide a notification thatmonitoring is active. The one or more processors may be caused by theinstructions to monitor data received from the plurality of smart homedevices to identify data indicative of behavior considered unusual basedon the ordinary behavior model. The one or more processors may be causedby the instructions to create an alert that identifies the behavior andidentifies how the behavior contrasts with the ordinary behavior model.The one or more processors may be caused by the instructions to cause tobe sent, to an administrator device linked with the user account, thealert that identifies the behavior and identifies how the behaviorcontrasts with the ordinary behavior model.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of variousembodiments may be realized by reference to the following figures. Inthe appended figures, similar components or features may have the samereference label.

FIG. 1 illustrates an embodiment of a system for performing a confidenceassessment of whether smart home devices can effectively monitor anelderly resident and monitoring the elderly resident's daily behavior.

FIG. 2 illustrates an embodiment of a smart-home environment.

FIG. 3 illustrates a network-level view of an extensible devices andservices platform with which a plurality of smart-home environments canbe integrated.

FIG. 4 illustrates an embodiment of method for performing a confidenceassessment of whether a suite of smart home devices can effectivelymonitor an environment for the behavior pattern of a resident and formonitoring the home environment following the confidence assessment.

FIG. 5 illustrates an embodiment of a method for performing a confidenceassessment of whether a suite of smart home devices can effectivelymonitor a home environment for the behavior pattern of a resident.

FIG. 6 illustrates an embodiment of a method for monitoring a homeenvironment to determine if a resident's behavior is sufficientlyworrisome to warrant an administrator being notified.

FIG. 7 illustrates an embodiment of a notification that can be providedto an administrator based on the behavior of the resident in theenvironment being monitored.

DETAILED DESCRIPTION

A monitoring arrangement that allows an administrator, such as an adultchild, to be made aware of worrisome behavior of a parent may be usefulto both the adult child and the parent. The adult child may be providedwith peace of mind knowing that her parent is being monitored and thatshe will receive a notification if the parent's behavior is abnormal.The parent may have peace-of-mind knowing that someone will be alertedif her behavior is out of the ordinary. While the following descriptionfocuses on an “administrator,” such as an adult child, monitoring aresident of a residence (e.g., an elderly parent) from afar, it shouldbe understood that the arrangements detailed herein can be applied toany form of monitoring of a first person (resident) by a second person(administrator) The following description assumes that, of course, theresident or the resident's legal guardian has granted the administratorauthorization to perform such monitoring. For instance, two friends mayagree that they should monitor each other since they both live alone andare concerned about falling ill with no one to check on them. As anotherexample, a sister may monitor her brother who lives alone but has amental, physical, or medical disability.

In order to provide monitoring of a resident, it may be beneficial toavoid installing a suite of specialty sensors in the residence of theresident. Rather, sensors that are already in place or are being usedfor additional tasks may be used to effect monitoring. For instance,various smart home devices (e.g., smart thermostats, smart smokedetectors, smart carbon monoxide detectors, home assistant devices,smart doorbell devices, indoor security cameras, and outdoor securitycameras) may have on-board sensors, such as cameras, motion sensors(e.g., passive infrared sensors), and microphones, that are sufficientto provide some amount of monitoring of the elderly parent.

An initial determination may be made as to whether the smart homedevices installed at the residence of the resident are sufficient toprovide monitoring of the resident. This initial determination may be aconfidence assessment that is used to determine whether the datareceived from the smart home devices is sufficient to monitor thebehavior of the resident with at least a threshold level of confidence.While such a system may not be designed for medical monitoring (e.g., toprovide immediate access to emergency medical care), the administrator,such as the adult child, may desire a low number of false positives onabnormal behavior of the resident, such as the parent. As such, thesystem must be able to assess the resident's behavior with a high degreeof confidence.

If the confidence assessment indicates that the smart home deviceswithin the residence of the resident are sufficient to monitor theresident's behavior with a high degree of accuracy, an ordinary behaviormodel (OBM) may be created that details the resident's typical dailybehavior pattern. The resident's daily activity may be compared to theOBM to determine if the resident's activity is sufficiently differentfrom the OBM to warrant the administrator being notified. For example,the OBM may detail a time period during the day during which at leastsome motion is typically detected (e.g., from 9 AM-7 PM). Theadministrator may only be notified for sufficiently worrisome behavior,which may include specific behaviors that the administrator hasidentified as concerning and is sufficiently different from the behaviorindicated in the OBM. For instance, no movement may occur during theentire time period. If the administrator is to be notified of unusualbehavior by the resident, a notification may be sent to theadministrator indicative of the unusual behavior and, possibly, acomparison with the typical behavior indicated in the OBM. For instance,a notification may state: “No movement has been detected at the home of[Resident], which is out of the ordinary. Touch here to learn furtherdetails.”

Additional detail regarding such arrangements is provided in relation tothe Figures. FIG. 1 illustrates an embodiment of a system for performinga confidence assessment of whether smart home devices can effectivelymonitor a resident and for monitoring the resident's daily behavior.System 100 may be composed of one or more computer server systems andmay include: confidence assessment engine 110, confidence assessmentrules 111, user account database 112, smart home device database 113,smart device data reception interface 120, environment learning engine130, ordinary behavior model database 131, environment monitoring engine140, recent behavior log 141, and notification engine 150. The variouscomponents of system 100 may be implemented using one or more computerserver systems. The various engines described in relation to system 100may be implemented using stored instructions executed by one or moregeneral-purpose processors. Alternatively, one or more special purposeprocessors may be configured to perform the functions of the variousengines of system 100. The various databases and logs may be storedusing one or more non-transitory computer readable mediums, such asmemory, hard drives, and/or solid state drives.

Confidence assessment engine 110 may receive data from smart device datareception interface 120 and may access various databases including:confidence assessment rules 111, user account database 112, and smarthome device database 113. Confidence assessment engine 110 may determinewhether sufficient data and consistent enough data is received fromsmart device data reception interface 120 in order for system 100 toprovide sufficiently accurate monitoring of a resident living in aresidence. Confidence assessment engine 110 may determine a level ofconfidence by assessing various factors including: the number of smarthome devices installed in the residence that can provide monitoringservices; the type of monitoring sensors available in the smart homedevices installed in the residence (e.g., video cameras, passiveinfrared sensors, microphones), the number of smart home devices thatare exclusively powered by battery (which may have stringent powerrequirements and may not be able to perform wireless communication asfrequently as a line powered smart home device), the specific locationof the smart home devices in relation to the resident being monitored(e.g., within the bedroom in which the resident sleeps, in a kitchenused by the resident daily), the size of the residence being monitored,whether the resident has a smart phone typically carried on theresident's body, the answers to a questionnaire provided either to theadministrator and/or the resident, and/or the consistency of datareceived from smart devices data reception engine 120 during a learningtime period. A confidence metric may be calculated and compared to athreshold based on at least some of the above factors. If the metricexceeds a threshold value, the residence may be eligible for monitoringof the resident. Certain factors may make the confidence metricirrelevant. For example, various “if-else” logic may be used todisqualify a resident or residence from monitoring. For example, if theresident does not have a smartphone, the resident may be disqualifiedfrom monitoring regardless of the calculated metric.

Confidence assessment engine 110 may provide data to notification engine150 indicative of whether or not a sufficiently high confidence ispresent that system 100 could accurately monitor the behavior of anelderly resident residing at the residence. If not, notification engine150 may cause the ability to enable elderly monitoring to be disabled,such as in an application executed on a mobile device of anadministrator. If the confidence is sufficiently high, such as acalculated confidence metric is greater than a stored threshold, elderlymonitoring may be enabled or an option to enable such elderly monitoringmay be provided to the administrator.

Confidence assessment rules 111 may be a set of rules that are used toevaluate the smart home devices present at the residence to be monitoredand to evaluate responses to a questionnaire provided by either theresident or the administrator, such as via an application executed by amobile device. Using confidence assessment rules 111, a confidencemetric may be calculated. If the metric is at least a threshold value,monitoring may be available; otherwise, it may not be available.Examples of various rules which may be a part of confidence assessmentrules 111 can include: Is at least one non-battery powered smart devicemonitoring a location frequented by the resident within the residence?If the answer is no, no further assessment may be performed andmonitoring may not be available. Another rule may involve determiningwhat is the number of smart home devices present in the residence? Themetric score may be increased based on the number of devices present. Ifat least a threshold number (e.g., 2) of devices is not present,monitoring may not be available.

User account database 112 may store indications of various user accountsthat are linked with smart home devices indicated in smart home devicedatabase 113. Each smart home device of a particular manufacturer orthat is compatible with a particular standard may require to beregistered with system 100. As part of registration, the smart homedevice may be linked with a particular user account. A user account maybe linked with some number of smart devices, a user name, and apassword. Accessing the user account, such as from an application orweb-based login, may provide access to data gathered from the varioussmart devices linked with the user account. Each smart home devicepresent at a residence to be monitored may be linked with a user accountof the elderly resident. Each administrator, such as an adult child,that may be planning on monitoring a resident may have her own account(which may or may not also be linked with other smart home devices).This account may be linked with the resident's account for monitoringpurposes based upon the resident providing consent to monitoring via anauthorization process, which may be offered through an applicationexecuted by the resident's mobile device (e.g., smartphone).

Smart home device database 113 may be linked with user account database112 such that an identifier of each smart home device (e.g., MACaddress) is linked with a user account of user account database 112. Insome embodiments, these two databases may be a single database. Thesmart home device database 113 may link a particular smart home deviceto data gathered from the smart home device and, possibly, processed bysystem 100 or some other system. For example, sensor data gathered froma smart home device may be used to determine if a person was at home ata given time. This indication of the person being home or away may bestored in smart home device database 113 or otherwise linked to thesmart home device and linked user account.

Smart devices data reception interface 120 may serve to receive datafrom various types of smart home devices for storage and for processing.Data received by smart device data reception interface 120 may be storedin relation to the linked user account and/or in relation to theparticular smart home device as part of smart home device database 113.Data received by smart device data reception interface 120 may be usedby confidence assessment engine 110 in performing the confidenceassessment. Data received by smart device data reception interface 120may be assessed by confidence assessment engine 110 for repeatingpatterns present in the data indicative of the resident's behavior. Forinstance, a resident being detected as rising from bed within a giventime range each day (e.g., a hour or two hour range) may increase theconfidence metric, but if the resident is erratic in his sleepingpattern (e.g., rising from bed at a variety of times that exceed the oneor two hour range) when the confidence assessment is being performed,the confidence metric may be decreased or not increased.

Environment learning engine 130 may use data from smart device datareception interface 120 to create an ordinary behavior model for aresidence and the resident who lives at the residence. Sensor datareceived by smart device data reception interface 120 from smart homedevices present within the resident's residence may be monitored for a:wake-up time period (a time period during which there is a highlikelihood (i.e., x % chance) that the resident gets out of bed); abedtime time period (a time period during which there is a highlikelihood (i.e., x % chance) that the resident goes to bed); and/or anactivity interval (an amount of time during which movement by theresident is expected to be observed at least a threshold number of times(e.g., movement at least once per hour between the wake-up time periodand the bedtime time period). The higher the percent chance set by theadministrator or predefined at system 100, the greater the time periodmay be in duration, which may decrease the number of false positives ofworrisome behavior, but may result in some delayed or missed instancesof worrisome behavior.

Environmental learning engine 130 may use location data gathered from asmartphone, cellular phone, or other electronic device that the residenttypically carries when leaving the residence to determine when theresident has left and/or returned to the residence. Most residents, forexample, may be expected to bring a phone with them if they leave home.The times that the resident leaves, the duration of time for which theresident is gone, and the times that the resident returns to theresidence (or a geo-fenced region encompassing the residence) may beused to create the OBM.

An OBM may be created over a period of time, such as a month. This OBMmay include data such as the wake-up time period, the bedtime timeperiod, and an activity interval. Of course, other behavioral patternsmay be stored as part of the OBM. Various behavioral patterns may bemonitored by environment learning engine 130 based on which smart homedevices are present at the residence. For instance, the OBM may includean indication of how often a resident leaves a residence if an outdoorsecurity camera or smart doorbell device is present to monitor personsentering and exiting the residence. The outdoor behavior model createdby environment learning engine 130 may be stored to ordinary behaviormodel database 131 and may be linked with the resident via theresident's user account or the administrator's account. Environmentlearning engine 130 may continue to observe and update the OBM based onthe resident's behavior over time. For instance, if over time theresident's bedtime becomes earlier, the bedtime time period may beshifted to reflect this earlier bedtime that has changed slowly overtime.

In some embodiments, environment learning engine 130 may only beactivated following confidence assessment engine determining that theresidence can be monitored with a high degree of confidence. In otherembodiments, environment learning engine 130 may attempt to create anordinary behavior model for the elderly resident living at the residenceprior to the confidence metric being computed by confidence assessmentengine 110. If a repeated pattern of behavior is observed by environmentlearning engine 130, data may be provide by environment learning engine130 to confidence assessment engine 110 that increases the confidencemetric. If the behavior observed by environmental learning engine 130does not correlate closely to a pattern, data may be provided byenvironment learning engine 130 to confidence assessment engine 110 thatdoes not increase the confidence metric or decreases the confidencemetric.

Once an ordinary behavior model has been created and monitoring has beenenabled due to the confidence assessment exceeding a thresholdconfidence value, environment monitoring engine 140 may monitor theresident's behavior within the residence to determine if it complieswith the ordinary behavior model for the resident stored within ordinarybehavior model database 131. Environment monitoring engine 140 maymaintain a recent behavior log 141 that reflects recent behavior of theresident, such as the resident's recent actions within the last day orweek. Environment monitoring engine 140 may receive data from smartdevice data reception interface 120 and may monitor this data todetermine when particular behaviors has been performed by the resident,such as when the resident has risen from bad, when has the resident mostrecently been active, and when has the resident gone to bed. Thesebehaviors may be compared with the ordinary behavior model stored aspart of the ordinary behavior model database 131. If the behaviorexceeds a time period for the behavior specified within the OBM or thebehavior exceeds the time period specified by the OBM by more than athreshold amount (e.g., an hour), the behavior may be a candidate fornotification to an administrator, such as an adult child.

Notification engine 150 may serve to communicate with the administratorand the resident. Notification engine 150 may access user accountdatabase 112 to determine an administrator to notify if there isabnormal behavior by a resident. Notification engine 150 may output anotification in response to a behavior determined to be abnormal basedon the behavior being outside of the OBM for the resident. Such anotification may include an indication of the abnormal behavior and acomparison with the OBM. The notification may be provided to theadministrator in the form of a push notification and/or message withinan application executed by a mobile device of the administrator.Notification engine 150 may have access to notification rules database151. Notification rules database 151 may be used to determine when anadministrator is to be notified. For instance, an administrator maycustomize which situations he believes are worrisome. For example, anadministrator may not wish to be notified about periods of inactivity,but may wish to be notified if the resident's bedtime or wake time isoutside of norms. Therefore, notification rules of notification rulesdatabase 151 may be linked with a particular user account of useraccount database 112. In some embodiments, a notification to be sent toan administrator is first sent to a device of the resident. If theresident does not respond within a defined time period or provides anunsatisfactory answer, the notification may be sent by notificationengine 150 to the administrator.

System 100 may be implemented as part of a cloud-based server systemthat is remote from either the residence of the resident or theadministrator. Alternatively, some or all of the functionality of system100 may be implemented locally by one or more devices located at theresidence. For instance, a smart home device (e.g., a home assistant)that performs some of the monitoring of the resident may have system 100incorporated as part of it. System 100 may be incorporated as any of thesmart home devices detailed in relation to smart-home environment 200 ormay be part of cloud-computing system 264.

FIG. 2 illustrates an embodiment of a smart-home environment. FIG. 2illustrates an example of a smart-home environment 200 with which system100 may interact to learn the behaviors of a resident and monitor theresident. In some embodiments, it may be required that the resident livealone. In other embodiments, the sensors may be robust enough thatmultiple residents can be disambiguated. Environment 200 can reflect aresidence in which a resident lives and is being monitored by varioussensors present on smart home devices. The depicted smart-homeenvironment 200 includes a structure 250, which can include, e.g., ahouse, office building, garage, or mobile home. It will be appreciatedthat devices can also be integrated into a smart-home environment 200that does not include an entire structure 250, such as an apartmentunit, condominium, townhome, or office space. Further, the smart homeenvironment can control and/or be coupled to devices outside of theactual structure 250. Indeed, several devices in the smart homeenvironment need not physically be within the structure 250 at all. Forexample, a device controlling a pool heater or irrigation system can belocated outside of the structure 250.

The depicted structure 250 includes a plurality of rooms 252, separatedat least partly from each other via walls 254. The walls 254 can includeinterior walls or exterior walls. Each room can further include a floor256 and a ceiling 258. Smart home devices can be mounted on, integratedwith and/or supported by a wall 254, floor 256 or ceiling 258.

In some embodiments, the smart-home environment 200 of FIG. 2 includes aplurality of devices, including intelligent, multi-sensing,network-connected devices, that can integrate seamlessly with each otherand/or with a central server or a cloud-computing system to provide anyof a variety of useful smart-home objectives. The smart-home environment200 may include one or more intelligent, multi-sensing,network-connected thermostats 202 (hereinafter referred to as “smartthermostats 202”), one or more intelligent, network-connected, hazarddetectors 204, and one or more intelligent, multi-sensing,network-connected entryway interface devices 206 (hereinafter referredto as “smart doorbells 206”). According to embodiments, the smartthermostat 202 detects ambient climate characteristics (e.g.,temperature and/or humidity) and controls a HVAC system 203 accordingly.The hazard detector 204 may detect the presence of a hazardous substanceor a substance indicative of a hazardous substance (e.g., smoke, fire,or carbon monoxide). Smart thermostats 202 and hazard detector 204 mayeach also detect occupancy, such as by using one or more on-boardpassive infrared sensors. The smart doorbell 206 may detect a person'sapproach to or departure from a location (e.g., an outer door) using acamera and/or passive infrared (PIR) sensor, control doorbellfunctionality, announce a person's approach or departure via audio orvisual means, or control settings on a security system (e.g., toactivate or deactivate the security system when occupants go and come).

In some embodiments, the smart-home environment 200 of FIG. 2 furtherincludes one or more intelligent, multi-sensing, network-connected wallswitches 208 (hereinafter referred to as “smart wall switches 208”),along with one or more intelligent, multi-sensing, network-connectedwall plug interfaces 210 (hereinafter referred to as “smart wall plugs210”). The smart wall switches 208 may detect ambient lightingconditions, detect actuation, detect room-occupancy states (e.g.,movement of a resident), and control a power and/or dim state of one ormore lights. In some instances, smart wall switches 208 may also controla power state or speed of a fan, such as a ceiling fan. The smart wallplugs 210 may detect occupancy of a room or enclosure and control supplyof power to one or more wall plugs (e.g., such that power is notsupplied to the plug if nobody is at home).

Still further, in some embodiments, the smart-home environment 200 ofFIG. 2 includes a plurality of intelligent, multi-sensing,network-connected appliances 212 (hereinafter referred to as “smartappliances 212”), such as refrigerators, stoves and/or ovens,televisions, washers, dryers, lights, stereos, intercom systems,garage-door openers, floor fans, ceiling fans, wall air conditioners,pool heaters, irrigation systems, security systems, and so forth.According to embodiments, the network-connected appliances 212 are madecompatible with the smart-home environment by cooperating with therespective manufacturers of the appliances. Such smart appliances may beable to detect movement in their vicinity and/or interactions with thesmart appliances by a resident. For example, the appliances can be spaceheaters, window AC units, motorized duct vents, etc. When plugged in, anappliance can announce itself to the smart-home network, such as byindicating what type of appliance it is, and it can automaticallyintegrate with the controls of the smart-home. Such communication by theappliance to the smart home can be facilitated by any wired or wirelesscommunication protocols known by those having ordinary skill in the art.The smart home also can include a variety of non-communicating legacyappliances 240, such as old conventional washer/dryers, refrigerators,and the like which can be controlled, albeit coarsely (ON/OFF), byvirtue of the smart wall plugs 210. The smart-home environment 200 canfurther include a variety of partially communicating legacy appliances242, such as infrared (“IR”) controlled wall air conditioners or otherIR-controlled devices, which can be controlled by IR signals provided bythe hazard detectors 204 or the smart wall switches 208.

According to embodiments, the smart thermostats 202, the hazarddetectors 204, the smart doorbells 206, the smart wall switches 208, thesmart wall plugs 210, and other devices of the smart-home environment200 are modular and can be incorporated into older and new houses. Forexample, the devices are designed around a modular platform consistingof two basic components: a head unit and a back plate, which is alsoreferred to as a docking station. Multiple configurations of the dockingstation are provided so as to be compatible with any home, such as olderand newer homes. However, all of the docking stations include a standardhead-connection arrangement, such that any head unit can be removablyattached to any docking station. Thus, in some embodiments, the dockingstations are interfaces that serve as physical connections to thestructure and the voltage wiring of the homes, and the interchangeablehead units contain all of the sensors, processors, user interfaces, thebatteries, and other functional components of the devices.

In some embodiments, one or more smart indoor security cameras may bepresent such as indoor security camera 272. Indoor security camera 272may wirelessly communicate with a cloud server system to capture andrecord video and audio. Indoor security camera 272 may be able to detectmotion, recognize a resident (e.g., via facial detection), and detectthe presence of a resident or other person via audio (e.g., detection ofa human voice). Indoor and outdoor security cameras may be used todetermine when a resident leaves home (for example, an OBM behavior maybe a time range during the day when a resident typically leaves home,such as between 8 AM and 6 PM) or returns home. In some embodiments, aspreviously detailed, this data may be supplemented with location dataderived from an electronic device, such as a smartphone, that a residenttypically carries when going out. If the smartphone is forgotten by theresident, data from the cameras may be used to determine that thesmartphone has been left behind and the resident has left the residence(e.g., a camera detects the resident leaving, the smartphone remainsstationary in the residence, and there is no movement detected withinthe residence for a threshold period of time). When the environment isdarkened, indoor security camera 272 may use infrared to detect thepresence of a resident and/or other persons in the camera'sfield-of-view.

In some embodiments, one or more smart outdoor security cameras may bepresent such as outdoor security camera 276. Outdoor security camera 276may wirelessly communicate with a cloud server system to capture andrecord video and audio and may function similarly to indoor securitycamera 272. Outdoor security camera 276 may include weatherproofing toprotect against the outdoor environment. Outdoor security camera 276 maybe able to detect motion, recognize a resident (e.g., via facialdetection), and detect the presence of a resident or other person viaaudio (e.g., detection of a human voice). At night, outdoor securitycamera 276 may use infrared to detect the presence of a resident and/orother persons in the camera's field-of-view.

In some embodiments, one or more home assistant devices may be presentin the residence, such as home assistant device 274. Home assistantdevice 274 may include one or more microphones. Home assistant device274 may detect and analyze human speech and may be able to detect speechand/or movement by the resident. Further, home assistant device 274 maybe able to make inquiries of the resident. For instance, if the residenthas not been detected as moving in a predefined period of time by any ofthe smart home devices within the residence, home assistant device 274may ask “[Resident], are you OK?” If no response is received, this maytrigger a notification being sent to an administrator.

The smart-home environment 200 may also include communication withdevices outside of the physical home but within a proximate geographicalrange of the home. For example, the smart-home environment 200 mayinclude a pool heater monitor 214 that communicates a current pooltemperature to other devices within the smart-home environment 200 orreceives commands for controlling the pool temperature. Similarly, thesmart-home environment 200 may include an irrigation monitor 216 thatcommunicates information regarding irrigation systems within thesmart-home environment 200 and/or receives control information forcontrolling such irrigation systems. According to embodiments, analgorithm is provided for considering the geographic location of thesmart-home environment 200, such as based on the zip code or geographiccoordinates of the home. The geographic information is then used toobtain data helpful for determining optimal times for watering; suchdata may include sun location information, temperature, due point, soiltype of the land on which the home is located, etc. These devices may beable to detect and report input provided by a resident.

By virtue of network connectivity, one or more of the smart-home devicesof FIG. 2 can further allow a resident to interact with the device evenif the resident is not proximate to the device. For example, a residentcan communicate with a device using a computer (e.g., a desktopcomputer, laptop computer, or tablet) or other portable electronicdevice (e.g., a smartphone) 266. A webpage or app can be configured toreceive communications from the resident and control the device based onthe communications and/or to present information about the device'soperation to the resident. For example, the resident can view a currentsetpoint temperature for a device and adjust it, using a computer. Theresident can be in the structure during this remote communication oroutside the structure.

As discussed, users can control and interact with the smart thermostat,hazard detectors 204, and other smart devices in the smart-homeenvironment 200 using a network-connected computer or portableelectronic device 266. In some examples, some or all of the residents(e.g., individuals who live in the home) can register their electronicdevice 266 with the smart-home environment 200. Such registration can bemade at a cloud-based server (as detailed in relation to system 100) toauthenticate the resident and/or the device as being associated with thehome and to give permission to the resident to use the device to controlthe smart devices in the home. A resident can use their registeredelectronic device 266 to remotely control the smart devices of the home,such as when the resident is at work or on vacation. The resident mayalso use their registered device to control the smart devices when theresident is actually located inside the home, such as when the residentis sitting on a couch inside the home. It should be appreciated that,instead of or in addition to registering electronic devices 266, thesmart-home environment 200 makes inferences about which individuals livein the home and are therefore residents and which electronic devices 266are associated with those individuals. As such, the smart-homeenvironment “learns” who is an resident and permits the electronicdevices 266 associated with those individuals to control the smartdevices of the home.

In some embodiments, in addition to containing processing and sensingcapabilities, each of the devices 202, 204, 206, 208, 210, 212, 214,216, 272, 274, and 276 (collectively referred to as “the smart devices”)is capable of data communications and information sharing with any otherof the smart devices, as well as to any central server orcloud-computing system or any other device that is network-connectedanywhere in the world. The required data communications can be carriedout using any of a variety of custom or standard wireless protocols(Wi-Fi, ZigBee, 6LoWPAN, etc.) and/or any of a variety of custom orstandard wired protocols (CAT6 Ethernet, HomePlug, etc.)

According to embodiments, all or some of the smart devices can serve aswireless or wired repeaters. For example, a first one of the smartdevices can communicate with a second one of the smart devices via awireless router 260. The smart devices can further communicate with eachother via a connection to a network, such as the Internet 299. Throughthe Internet 299, the smart devices can communicate with acloud-computing system 264, which can include one or more centralized ordistributed server systems. The cloud-computing system 264 can beassociated with a manufacturer, support entity, or service providerassociated with the device. For one embodiment, a user may be able tocontact customer support using a device itself rather than needing touse other communication means such as a telephone or Internet-connectedcomputer. Further, software updates can be automatically sent fromcloud-computing system 264 to devices (e.g., when available, whenpurchased, or at routine intervals).

According to embodiments, the smart devices combine to create a meshnetwork of spokesman and low-power nodes in the smart-home environment200, where some of the smart devices are “spokesman” nodes and othersare “low-powered” nodes. Some of the smart devices in the smart-homeenvironment 200 are battery powered, while others have a regular andreliable power source, such as by connecting to wiring (e.g., to 120 Vline voltage wires) behind the walls 254 of the smart-home environment.The smart devices that have a regular and reliable power source arereferred to as “spokesman” nodes. These nodes are equipped with thecapability of using any wireless protocol or manner to facilitatebidirectional communication with any of a variety of other devices inthe smart-home environment 200 as well as with the cloud-computingsystem 264. On the other hand, the devices that are battery powered arereferred to as “low-power” nodes. These nodes tend to be smaller thanspokesman nodes and can only communicate using wireless protocols thatrequire very little power, such as ZigBee, 6LoWPAN, etc. Further, some,but not all, low-power nodes are incapable of bidirectionalcommunication. These low-power nodes send messages, but they are unableto “listen”. Thus, other devices in the smart-home environment 200, suchas the spokesman nodes, cannot send information to these low-powernodes.

As described, the smart devices serve as low-power and spokesman nodesto create a mesh network in the smart-home environment 200. Individuallow-power nodes in the smart-home environment regularly send outmessages regarding what they are sensing, and the other low-powerednodes in the smart-home environment—in addition to sending out their ownmessages—repeat the messages, thereby causing the messages to travelfrom node to node (i.e., device to device) throughout the smart-homeenvironment 200. The spokesman nodes in the smart-home environment 200are able to “drop down” to low-powered communication protocols toreceive these messages, translate the messages to other communicationprotocols, and send the translated messages to other spokesman nodesand/or cloud-computing system 264. Thus, the low-powered nodes usinglow-power communication protocols are able to send messages across theentire smart-home environment 200 as well as over the Internet 263 tocloud-computing system 264. According to embodiments, the mesh networkenables cloud-computing system 264 to regularly receive data from all ofthe smart devices in the home, make inferences based on the data, andsend commands back to one of the smart devices to accomplish some of thesmart-home objectives described herein.

As described, the spokesman nodes and some of the low-powered nodes arecapable of “listening.” Accordingly, users, other devices, andcloud-computing system 264 can communicate controls to the low-powerednodes. For example, a user can use the portable electronic device (e.g.,a smartphone) 266 to send commands over the Internet to cloud-computingsystem 264, which then relays the commands to the spokesman nodes in thesmart-home environment 200. The spokesman nodes drop down to a low-powerprotocol to communicate the commands to the low-power nodes throughoutthe smart-home environment, as well as to other spokesman nodes that didnot receive the commands directly from the cloud-computing system 264.

An example of a low-power node is a smart nightlight 270. In addition tohousing a light source, the smart nightlight 270 houses an occupancysensor, such as an ultrasonic or passive IR sensor, and an ambient lightsensor, such as a photo-resistor or a single-pixel sensor that measureslight in the room. Such a nightlight 270 may be able to detect when aresident moves nearby. In some embodiments, the smart nightlight 270 isconfigured to activate the light source when its ambient light sensordetects that the room is dark and when its occupancy sensor detects thatsomeone is in the room. In other embodiments, the smart nightlight 270is simply configured to activate the light source when its ambient lightsensor detects that the room is dark. Further, according to embodiments,the smart nightlight 270 includes a low-power wireless communicationchip (e.g., ZigBee chip) that regularly sends out messages regarding theoccupancy of the room and the amount of light in the room, includinginstantaneous messages coincident with the occupancy sensor detectingthe presence of a person in the room. As mentioned above, these messagesmay be sent wirelessly, using the mesh network, from node to node (i.e.,smart device to smart device) within the smart-home environment 200 aswell as over the Internet 299 to cloud-computing system 264.

Other examples of low-powered nodes include battery-operated versions ofthe hazard detectors 204. These hazard detectors 204 are often locatedin an area without access to constant and reliable (e.g., structural)power and, as discussed in detail below, may include any number and typeof sensors, such as smoke/fire/heat sensors, carbon monoxide/dioxidesensors, occupancy/motion sensors, ambient light sensors, temperaturesensors, humidity sensors, and the like. Furthermore, hazard detectors204 can send messages that correspond to each of the respective sensorsto the other devices and cloud-computing system 264, such as by usingthe mesh network as described above.

Examples of spokesman nodes include smart doorbells 206, smartthermostats 202, smart wall switches 208, and smart wall plugs 210.These devices 202, 206, 208, and 210 are often located near andconnected to a reliable power source, and therefore can include morepower-consuming components, such as one or more communication chipscapable of bidirectional communication in any variety of protocols.

According to one embodiment, the user can be provided with a suite ofrelated smart-home devices, such as may be provided by a commonmanufacturer or group or badged to work with a common “ecosystem” ofthat manufacturer or group, wherein each of the devices, wherepracticable, provides a same or similarly triggered illumination-basednotification scheme and theme, such that the user can be readilyfamiliar with the status signals emitted by the variety of differentdevices without needing to learn a different scheme for each device.Thus, by way of example, there can be provided a suite of devicesincluding a security/automation hub, multiple door/window sensors, andmultiple hazard detectors, wherein each such device has a circularillumination ring that conveys triggered visual information according tothe themes and schemes described herein.

FIG. 3 illustrates a network-level view of an extensible devices andservices platform 300 with which a plurality of smart-home environments,such as smart-home environment 200 of FIG. 2, can be integrated. Theextensible devices and services platform 300 includes cloud-computingsystem 264, which may include system 100 of FIG. 1. Each of theintelligent, network-connected devices from FIG. 2 may communicate withcloud-computing system 264. For example, a connection to the Internet299 can be established either directly (for example, using 3G/4Gconnectivity to a wireless carrier), through a hubbed network 312 (whichcan be a scheme ranging from a simple wireless router, for example, upto and including an intelligent, dedicated whole-home control node), orthrough any combination thereof

Although in some examples provided herein, the devices and servicesplatform 300 communicates with and collects data from the smart devicesof smart-home environment 200 of FIG. 2, it should be appreciated thatthe devices and services platform 300 communicates with and collectsdata from a plurality of smart-home environments across the world. Forexample, cloud-computing system 264 can collect home data 302 from thedevices of one or more smart-home environments, where the devices canroutinely transmit home data or can transmit home data in specificinstances (e.g., when a device queries the home data 302). Thus, thedevices and services platform 300 routinely collects data from homesacross the world. As described, the collected home data 302 includes,for example, power consumption data, occupancy data, HVAC settings andusage data, carbon monoxide levels data, carbon dioxide levels data,volatile organic compounds levels data, sleeping schedule data, cookingschedule data, inside and outside temperature humidity data, televisionviewership data, inside and outside noise level data, etc.

Cloud-computing system 264 can further provide one or more services 304,which may include the functionality of system 100 of FIG. 1. Theservices 304 can include, e.g., software updates, resident monitoring,customer support, sensor data collection/logging, remote access, remoteor distributed control, or use suggestions (e.g., based on collectedhome data 302 to improve performance, reduce utility cost, etc.). Dataassociated with the services 304 can be stored at cloud-computing system264 and cloud-computing system 264 can retrieve and transmit the data atan appropriate time (e.g., at regular intervals, upon receiving arequest from a user, etc.).

As part of services 304, user accounts may be maintained by thecloud-computing system 264. The user account may store subscriptioninformation, billing information, registration information, userpreferences, and/or other data associated with various smart-homedevices, such as one or more hazard detectors, installed within astructure that is linked with a user account. Occasionally, attention ofa user to his or her user account may be requested. In response to aquery from hazard detector 350 (or other smart-home device), a messagemay be transmitted by the cloud-computing system 264 to hazard detector350 (which may represent any of the previously described hazarddetectors) indicating that a status output by hazard detector 350 shouldindicate that a user is requested to log in to his or her user account.Further detail regarding the requested log may be transmitted by service304 to hazard detector 350. For instance, the reason for the requestedlogin may be expired payment information (such as an expired creditcard). The user can request detail on a status output by hazard detector350, which may be presented to the user as a color and animation outputvia a light of hazard detector 350. The request for detail may be byperforming a gesture within the vicinity of hazard detector 350. Aspoken message may then be output by hazard detector 350 indicating thatthe user is requested to log in to his account and may also indicate thereason of the payment information needing to be updated. As such, astatus check performed by hazard detector 350 may not only check thestatus of hazard detector 350 itself, but also the state of aremotely-maintained user account.

As illustrated in FIG. 3, an embodiment of the extensible devices andservices platform 300 includes a processing engine 306, which can beconcentrated at a single server or distributed among several differentcomputing entities without limitation. The processing engine 306 caninclude computerized engines (e.g., software executed by hardware)configured to receive data from devices of smart-home environments(e.g., via the Internet 299 or a hubbed network), to index the data, toanalyze the data and/or to generate statistics based on the analysis oras part of the analysis. The analyzed data can be stored as derived homedata 308.

Results of the analysis or statistics can thereafter be transmitted backto the device that provided home data used to derive the results, toother devices, to a server providing a webpage to a user of the device,or to other non-device entities. For example, use statistics, usestatistics relative to use of other devices, use patterns, and/orstatistics summarizing sensor readings can be generated by theprocessing engine 306 and transmitted. The results or statistics can beprovided via the Internet 299. In this manner, the processing engine 306can be configured and programmed to derive a variety of usefulinformation from the home data 302. A single server can include one ormore engines.

In some embodiments, to encourage innovation and research and toincrease products and services available to users, the devices andservices platform 300 exposes a range of application programminginterfaces (APIs) 310 to third parties, such as charities, governmentalentities (e.g., the Food and Drug Administration or the EnvironmentalProtection Agency), academic institutions (e.g., universityresearchers), businesses (e.g., providing device warranties or serviceto related equipment, targeting advertisements based on home data),utility companies, and other third parties. The APIs 310 may be coupledto and permit third-party systems to communicate with cloud-computingsystem 264, including the services 304, the processing engine 306, thehome data 302, and the derived home data 308. For example, the APIs 310allow applications executed by the third parties to initiate specificdata processing tasks that are executed by cloud-computing system 264,as well as to receive dynamic updates to the home data 302 and thederived home data 308.

Account alert engine may serve to determine whether a hazard detectorshould provide an indication that the user's account requires attention.For instance, account alert engine 305 may periodically assess the stateof a user's account, such as whether settings need updating, whetherpayment information is up-to-date, whether one or more messages arepending, whether payment is due, etc. If user attention is required,upon a request being received from a hazard detector and a look-up ofthe user's account being performed, account alert engine may respondwith an indication that the user account requires attention. Additionaldetail may also be provided such that if the user performs a gesture orotherwise requests additional detail, such detail can be provided, suchas via an auditory message. If user attention is not required, upon arequest being received from a hazard detector and a look-up of theuser's account being performed (e.g., by determining an accountassociated with the hazard detector from which the request wasreceived), account alert engine may respond with an indication that theuser account does not require attention.

FIG. 4 illustrates an embodiment of method 400 for performing aconfidence assessment of whether one or more smart home devices locatedat a residence can effectively monitor a home environment for thebehavior pattern of a resident and for monitoring the resident withinthe home environment following the confidence assessment. Method 400 maybe performed using system 100, which may be part of cloud computingsystem 264 of FIG. 2. Each step of method 400 may be performed by asmart home device and/or a cloud computing system that performs thefunctions of system 100 of FIG. 1.

As part of an initial configuration process 401, blocks 410-440 may beperformed. At block 410, a confidence assessment on smart home deviceslocated at a particular residence may be performed. Each of these smarthome devices may be required to be linked with a single user account.The confidence assessment of block 410 may be used to determine if theresidence is eligible for monitoring of a resident of a residence. Theconfidence assessment may include analyzing the number of smart homedevices, the types of smart home devices, the locations of smart homedevices, a questionnaire completed by an administrator or resident, etc.Further detail regarding the confidence assessment is provided inrelation to method 500 of FIG. 5.

At block 420, as a result of the confidence assessment of block 410, adetermination may be made as to whether the residence and the one ormore residents who live at the residence are eligible for monitoring.The determination of block 420 may be performed by comparing aconfidence metric calculated at block 410 with a confidence thresholdvalue that is predefined. Only if the confidence is above the confidencethreshold may monitoring of a resident be available. Below such aconfidence threshold it may be assumed that monitoring would provide toomany false positives of abnormal behavior or missed abnormal behaviorsof the resident. By maintaining at least a minimum confidence threshold,a quality of service for the notifications provided to an administratorcan be maintained.

If, at block 420, it is determined that the residence is not eligiblefor monitoring, occasionally, the confidence assessment may be performedagain to reassess. For example, the confidence assessment may beperformed again if one or more additional smart home devices are addedto the residence and linked with the user account. Block 430 may beperformed if at block 420 it is determined that the residence iseligible for monitoring. At block 430, a learning process may beperformed over a period of time. During this period of time, activitydata indicative of the resident's behavior is collected from the one ormore smart home devices that may be linked with a single user accountand are installed at the residence. This activity data may be used toconstruct an ordinary behavior model (OBM). The OBM may indicatebehaviors of the resident that are expected to be performed regularlyand can be accurately monitored using the smart home devices installedwithin the residence. Therefore, the behaviors included as part of theordinary behavior model may vary depending on the installed smart homedevices and the behavior patterns of the resident. Table 1 provides anexample of an OBM:

TABLE 1 Behavior Start Time End Time Resident rises from Bed 7:15 AM 8:40 AM Detect movement in kitchen 8:20 AM 10:15 AM Activate at leastone appliance 9:15 AM  4:27 PM in residence Periodically detect movement8:20 AM 10:15 PM within residence when resident is home Resident goes tobed 9:17 PM 10:43 PM

As indicated above, various behaviors may not be included in the OBM ifa smart home's devices are not present to monitor such a behavior. Forexample, determining whether at least one appliance is activated in theresidence may not be possible unless some or all of the appliances aresmart appliances or are connected with a smart outlet. In such aninstance, such a behavior may not be included as part of the OBM.Behaviors may only be included in the OBM if the behavior is regularlyperformed and is typically performed within a range of times. Forexample, over the course of the month, a resident may be monitored aspart of block 430 as rising between 7:30 and 8:30 AM. As part of theOBM, a small amount of buffer time may be added to account for minorvariations and the entry as indicated in Table 1 may be included in theOBM. If the behavior is detected as being performed, but is performederratically or not regularly, the behavior may not be included as partof the OBM because it may not be reliable in determining if the residentis behaving normally. In some embodiments, an OBM may detail the generalsense of activity in a home. Such a model may capture both commonactivity and uncommon activity. By having a model of all forms ofactivity, the OBM can be used to determine to what extent an activity isnormal or abnormal.

In Table 2, an alternate embodiment of an OBM is presented. In someembodiments, rather than an OBM defining an expected start and stop timeof various behaviors, the OBM may define whether a particular activityis likely or unlikely for a particular time period. The change inlikelihood may be continuous with time rather than occurring at discretetimes as indicated in Table 2. In some embodiments, different timeperiods may be associated with different behaviors. In Table 2, a “veryunlikely” behavior may correspond to less than a 10% chance of thatactivity occurring; an “unlikely” behavior” may correspond to less thana 50% of that activity occurring; a “likely” behavior may correspond toa more than 50% of that activity occurring; and a “very likely” behaviormay correspond to at least a 90% chance of that behavior occurring.

TABLE 2 Time Behavior Time Period 1 Time Period 2 Time Period 3 TimePeriod 4 Period 5 Movement Before 6 AM/ 6 AM-8 AM/ 8 AM to 6 6 PM to 11After 11 activity at unlikely Likely PM/ PM/very PM/ residence unlikelylikely unlikely Movement Before 6 AM/ 6 AM-8 AM/ 8 AM to 6 After 6 PM/outside geofence very unlikely unlikely PM/likely unlikely (e.g.,defining residence and/or local area around residence)

In some embodiments, creation of the OBM may be performed as part ofblock 410. The successful creation of an OBM that can monitor at leastsome behaviors of a resident may be a necessary condition of theconfidence assessment. In some embodiments, the number of behaviors thatcan be monitored and the narrower the time range that can be establishedfor performance of the behavior by the resident may affect thecalculated confidence metric. That is, the greater number of behaviorsthat can be included in the OBM, the smaller the time ranges for thebehaviors, and/or the ability to label an activity as likely orunlikely, the greater the confidence metric may be increased.

Creation of the OBM may be performed over a sufficient period of time,such as a week or month, that slightly varying behaviors by the residentcan be detected. For instance, a resident may rise later on weekend daysand may also tend to stay up later on weekend nights. In someembodiments, an administrator or resident may be able to provide inputthat directly affects the OBM, such as by directly inputting when theresident typically wakes up or goes to bed.

At block 440, in response to the OBM being successfully created,monitoring of the resident may be initiated. Such initiation may includea notification being provided to the administrator indicating thatmonitoring is now active. In some embodiments, the administrator may nowbe given the option to enable such monitoring. In some embodiments, atthis stage, the resident may be required to provide input via a useraccount of the resident that monitoring is permissible. In otherembodiments, such a permission may have been provided as part of orprior to blocks 410 and/or 430 being performed. Following block 440,method 400 may transition from the initial configuration process 401 tomonitoring process 402.

In some embodiments, the initial configuration may include monitoringthe resident using the various installed smart home devices present atthe residence. This monitoring may result in the generation of a body oftrial monitoring data. The duration of the trial time period may besufficient in length to capture multiple days or multiple repetitions ofa particular day of the week. For instance, a trial time period of fourweeks would capture how the resident behaves on four Mondays and fourSaturdays. A predefined threshold criterion may be set that defines athreshold percentage of the day that the trial data can exclusively beused to track the location of the resident within or in the immediatelyvicinity (e.g., in the residence's yard and/or courtyard) of theresidence, such as at least: 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97%, or99%. It should be understood that at block 420, in some instances, adetermination may be made that the smart home devices present in theresidence may not be sufficient in type, number, and/or location tosufficient track and monitor the resident through the resident's dailyand/or day-of-the-week specific activity for at least the predefinedthreshold criterion. In some embodiments, the predefined thresholdcriterion may additionally or alternatively be indicative of apercentage of each day that a resident's heartrate and/or breathing ratecan be monitored based on (exclusively) the received trial monitoringdata. In some embodiments, the trial period of time is at least: 7 days,14 days, 21 days, or 28 days. In some embodiments, default trial timeperiod is seven days. Such a confidence assessment may be carried outwith little or no user input as part of the confidence assessment. Inother embodiments, the trial time period is defined by theadministrator. The longer the duration of the trial time period, thegreater the confidence and/or more accurate the confidence assessmentmay be.

At block 450, data from the one or more smart home devices locatedwithin the residence and, possibly, linked with a single user account,may be monitored. This data may be monitored for indications of behaviorof the resident that are outside of the behavioral norms indicated bythe OBM. Therefore, multiple types of data may be monitored, includingmovement data from a motion sensor, audio from a microphone, videoindicative of the resident being present at a location or moving alocation, interactions with smart home devices, such as smart appliancesor smart home assistants, and/or location data from an electronic devicethat a resident tends to carry, such as a smartphone. Such location datamay be used for geofencing (e.g., to determine if the resident is withinthe residence, outside near the residence, or away from the residence).

Simply because a behavior does not fall within the norms established bythe OBM does not mean it is necessarily “worrisome” behavior. Whether abehavior is classified as “worrisome” may require the behavior to: 1)fall a threshold amount outside the norms established as part of theOBM; and 2) be a behavior that has been selected by the administrator asrequiring a notification. Regarding falling outside of the normsestablished as part of the OBM, there may be required that at least athreshold difference between the OBM and the observed behavior bepresent. For instance, if a wake up time of the resident of 7:12 AM isobserved and the time period indicated as normal within the OBM startsat 7:15 AM, an administrator may not want to be notified. Theadministrator or operator of system 100 may define a thresholddifference time between the time period established as part of the OBMand the observed behavior for the administrator to be notified, such asbetween 0 minutes and 120 minutes. Further, the behavior may be requiredto be selected as a behavior desired to be monitored. For example, anadministrator may wish to monitor wake times of the resident but not thetimes when the resident goes to bed or moves within the residence (e.g.,the resident may leave home frequently). The administrator or residentmay select, such as via a mobile application, which behaviors indicatedin the OBM are eligible for a notification. In some embodiments, theservice provider of system 100 may define default behaviors that areeligible for notifications and default other behaviors to not beeligible for notifications.

If no worrisome behavior is identified, block 450 may continue to beperformed to monitor behaviors of the resident. As block 450 is beingperformed, the OBM may continue to be updated to accommodate shiftingbehavior patterns over time. For example, the resident may tend to go tobed earlier in winter.

At block 460, if a behavior is sufficiently beyond the bounds of the OBMand the behavior corresponds to a behavior of which the administratorhas selected to be notified, a notification may be created at block 460.The notification may include text, audio, and/or video. The notificationmay indicate: 1) specifics of the observed behavior that is sufficientlyoutside the OBM by at least a threshold amount; and 2) the normalbehavior that the observed behavior is being compared with. For example,a notification may indicate that a resident is still in bed at 10:15 AM,but typically gets out of bed between 8:00 AM and 9:15 AM. At block 470,the notification may be sent to the administrator, such as via text, apush notification, or within an application. In other embodiments, acall or email may alternatively or additionally be used. An example ofsuch a notification is illustrated in FIG. 7. In some embodiments, aversion of the notification is first provided to the resident to allowthem an opportunity to respond or indicate he is OK, such as via text, aphone call, via a smart home assistant, or via a smartphone application.In some embodiments, in response to a notification such as a voiceprompt, the resident may be able to speak to a smart home deviceinstalled in the residence to indicate that he is OK. Additionally oralternatively, a resident may be permitted to perform a gesture orprovide another form of input to a smart home device in order to signalthat the resident is OK and does not require any other person to benotified. If no response is received to the notification is receivedfrom the resident, the administrator may be notified. FIG. 7 illustratesan embodiment of a smartphone 700 presenting a push message including anotification. This notification provides the administrator with theoption of contacting the resident (in this case, “Grandma”) via eithertext or a voice call or contacting a neighbor of the resident (e.g., tocheck on the resident).

In some embodiments, the action an administrator takes in response to anotification may be used to fine-tune the application of the OBM. If anadministrator repeatedly takes no action after a particular type ofnotification, sensitivity may be decreased such that fewer of suchnotifications are provided. Additionally or alternatively, anadministrator may have an option within an application's user interfaceto indicate that too many alerts (or too few alerts) are being received,possibly for a particular type of behavior. The application of the OBMmay be refined in response to such input.

FIG. 5 illustrates an embodiment of a method 500 for performing aconfidence assessment of whether a suite of smart home devices caneffectively monitor a home environment for the behavior pattern of anelderly occupant. Method 500 may be performed as part of block 410 ofmethod 400. Therefore, method 500 may be performed using system 100,which may be part of cloud computing system 264 of FIG. 2. Each step ofmethod 500 may be performed by a smart home device and/or a cloudcomputing system that performs the functions of system 100 of FIG. 1.

At block 510, a number of smart home devices that are present within theresidence of the resident to be monitored may be determined. Each ofthese smart home devices may be linked with a common user account at acloud-based server. For a smart home device to be identified aseligible, the smart home device may be required to have at least onesensor that can monitor for the presence of a person (e.g., a motionsensor, microphone, camera). Additionally or alternatively, to beeligible, a resident may be required to have a smartphone capable oflocation reporting.

At block 520, a number of the smart home devices identified at block 510may be identified as being powered exclusively by battery (or some otherform of energy-storage component). If a smart home device is poweredexclusively by battery, the amount of monitoring of the resident and/orthe frequency of data reporting to a remote device via wirelesscommunication that the smart home device can perform may be decreaseddue to power requirements. For example, a battery-powered device mayonly periodically or occasionally communicate wirelessly with acloud-based server or other smart home device in order to save power.Movement observed by a battery-powered device may not be reported asfrequently as movement detected by a line-powered smart home device. Thenumber of the smart home devices that are powered exclusively by batterymay be identified from the number of smart home devices determined atblock 510. The greater the percentage of battery-powered devices of thetotal number of smart home devices, the lower a confidence metric maybe. In some embodiments, at least one smart home device that regularlyobserves movement of the resident may be required to be line powered.

In some embodiments, at block 520 the analysis may be based on“power-constrained” devices, rather than just exclusivelybattery-powered devices. Some power-constrained devices may obtain powerfrom a source other than a battery, but may still have access to afinite amount of power at a given time, such as due to the way thedevice receives power. For example, a smart thermostat may “steal” powerfrom HVAC control and communication wires. The amount of power that canbe drawn from these wires may be significantly limited and may, thus,place the smart thermostat under significant power restrictions.Power-constrained devices can include devices that are poweredexclusively by battery.

At block 530, an administrator and/or the resident may be provided witha questionnaire. The questionnaire may be provided via an applicationexecuted by a mobile device the administrator or the resident. Answersto the questions of the questionnaire may determine whether theresidence is eligible for monitoring of the resident and/or may affectthe confidence metric computed as part of method 500. The followingquestions are examples of questions that may be posed to theadministrator or resident as part of the questionnaire: (1) Do you [orthe resident, as applicable] live alone?; (2) Do you have any pets, suchas a cat or dog?; (3) Is [smart home device] located in the bedroom yousleep in?; (4) Do you have a smartphone that you tend to carry when yougo out?; (5) Do you consent to being monitored by [administrator]?; and(6) How many square feet is the residence? The previous questions areonly examples of possible questions that may be asked as part of aquestionnaire. If question (1) is answered in the negative, theconfidence metric may be decreased or monitoring may not be available;if answered positively, the confidence metric may be increased and/ormonitoring may be available. If question (2) is answered in thenegative, the confidence metric may be increased and/or monitoring maybe available; if answered positively, the confidence metric may bedecreased and/or monitoring may not be available. Question (3) may be todetermine if a specific smart home device is located in a bedroom usedby the resident. For instance, a requirement of monitoring may be asmart home device with a motion sensor in or near the resident'sbedroom. Via another interface, it may already have been specified thata smart home device is in a type of room (e.g., bedroom), but it may beunknown if it is located in the primary bedroom where the resident tendsto sleep. Question (4) may be used to determine if a smartphone can beused for geo-fencing purposes. That is, determine when the resident hasleft the residence. Question (5) may be used to determine if theresident consents to monitoring by a person designated as theadministrator, such as an adult child. Question (6) may be used todetermine if sufficient smart home devices are present in the home toprovide an accurate picture of the behavior of the resident.

At block 540, in some embodiments, the residence may be monitored for aperiod of time to determine if sufficient data is being collected tocreate an accurate OBM. For instance, if the data collected by the smarthome devices cannot reliably be used to determine a rise time and bedtime for the resident, monitoring may be blocked or the confidencemetric may be decreased. Block 540 may be performed as part of block430. It may be required that a sufficient number of ordinary behaviorsbe identified for inclusion in the OBM. For instance, at least one ortwo behaviors that occur daily may be required to be repeatedlydetectable, such as the resident waking up, moving about the residence,or going to bed.

At block 550, a confidence metric may be calculated based on block510-540. Different point values (either positive or negative) may beassigned to the total number of smart home devices, the number of smarthome devices that are battery powered, the individual answers to thequestionnaire and the number of behaviors that can be accuratelydetected for inclusion in the OBM. Certain answer or conditions maypreclude any form of monitoring regardless of any confidence metriccalculation. For example, if the resident does not live alone,monitoring may not be available. If there is not a probation onmonitoring due a questionnaire answer or a result of a particular block,the confidence metric may be compared to a threshold confidence value atblock 420 of method 400.

If some non-zero number of exclusively battery-powered devices are to beused for monitoring a resident, code may be provided to and/or activatedat the battery-powered smart home device that modifies its communicationschedule with other smart home devices and/or a cloud-computing system,such as system 100. If certain behavior patterns are observed (or notobserved) by the battery-powered device, the battery-powered device maybe instructed to report the data other than at its periodic transmissionschedule. For instance, movement or non-movement that conflicts with alocally-stored version of the OBM by the battery-powered device maytrigger an immediate or ahead-of-schedule communication with system 100.For instance, a smart smoke detector may typically only connect to acloud-based server once every 23 hours. However, instructions in theform of code may be provided to the smart smoke detector that ifmovement is not sensed during one or more time periods, it should bereport the lack of movement immediately or at least sooner thanscheduled for communication with the cloud-based server. Occasionally,the battery-powered device may be provided with updated instructionsdefining when it should immediately or ahead of schedule report movementmeasurements, such as when a conflict with a provided version of the OBMis present.

FIG. 6 illustrates an embodiment of a method for monitoring a homeenvironment to determine if a resident's behavior is sufficientlyworrisome to warrant an administrator being notified. Method 600 may beperformed as part of block 450 of method 400. Therefore, method 600 maybe performed using system 100, which may be part of cloud computingsystem 264 of FIG. 2. Each step of method 600 may be performed by asmart home device and/or a cloud computing system that performs thefunctions of system 100 of FIG. 1.

At block 610, data may be received from the various smart home devicesbeing used to monitor the resident at the residence. At block 620, anactivity model for the resident may be created based on the receiveddata from the one or more smart home devices. While an OBM summarizes ormodels a resident's ordinary and/or out-of-the-ordinary behavior overtime, the activity log may be for a defined period of time, such as aparticular day. For instance, based on motion data from multiple smarthome devices (e.g., a smoke detector within the bedroom and a securitycamera in the living room), a time at which the resident rose from bedmay be indicated in the activity log. Therefore, an activity log maystore determinations that correspond to behaviors for which the OBM hasan entry.

At block 630, the activity model may be compared with the OBM toidentify any part of the activity model that differs from the normalranges indicated in the OBM. At block 640, a determination may be madeas to whether one or more items within the activity model differs by asufficient margin to warrant a notification being sent. Thresholdmargins may be established for the behaviors of the OBM or time rangesof the OBM itself may be expanded to create a margin. If the behavior ofthe activity model exceeds the margin or the expanded time range of theOBM, method 600 may proceed to block 650; otherwise, method 600 mayproceed back to block 610 and continue monitoring received data andfurther constructing the activity model.

At block 650, it may be determined if the administrator and/or residenthas enabled notifications for the particular worrisome behavior of theactivity model that does not match with the OBM. If enabled, method 600may proceed to block 460 of method 400. If notifications are not enabledfor the particular behavior of the activity model that does not matchwith the OBM, the notification may be logged and accessible via a mobiledevice application of the administrator and/or resident.

The OBM of the resident being monitored is discussed in one or moreembodiments above in terms of resident location within the residenceand/or interactions of the resident with their surrounding environment(e.g., activating an appliances), which have been found to representphenomena that, advantageously, are expository or “telling” of theoverall state of the resident while also being readily measurable by avariety of smart-home devices. However, it is to be appreciated that thescope of the present teachings includes measuring, using appropriatesensors, any of a variety of extrinsic and intrinsic states of theresident representative of their well-being, including, but not limitedto: medical well-being factors such as heartbeat, breathing rate, bloodglucose levels, blood oxygen levels, body temperature, quality ofspeech, quality of sleep, etc.; psychological well-being factors such ashow often the resident interacts with their pets, talks to visitors orpersons at the other end of the phone line or video conference, playsvideo games, etc.; and lifestyle well-being factors ranging from howoften they change their clothing to how regularly they do the dishes.For some embodiments, the user/administrator can select from a menu ofsuch observable characteristics to be included in the ordinary behaviormodel, and, as further smart-home devices are added (e.g., the additionof a remote heartbeat monitoring system), items can be added to themenu.

The methods, systems, and devices discussed above are examples. Variousconfigurations may omit, substitute, or add various procedures orcomponents as appropriate. For instance, in alternative configurations,the methods may be performed in an order different from that described,and/or various stages may be added, omitted, and/or combined. Also,features described with respect to certain configurations may becombined in various other configurations. Different aspects and elementsof the configurations may be combined in a similar manner. Also,technology evolves and, thus, many of the elements are examples and donot limit the scope of the disclosure or claims.

Specific details are given in the description to provide a thoroughunderstanding of example configurations (including implementations).However, configurations may be practiced without these specific details.For example, well-known circuits, processes, algorithms, structures, andtechniques have been shown without unnecessary detail in order to avoidobscuring the configurations. This description provides exampleconfigurations only, and does not limit the scope, applicability, orconfigurations of the claims. Rather, the preceding description of theconfigurations will provide those skilled in the art with an enablingdescription for implementing described techniques. Various changes maybe made in the function and arrangement of elements without departingfrom the spirit or scope of the disclosure.

Also, configurations may be described as a process which is depicted asa flow diagram or block diagram. Although each may describe theoperations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be rearranged. A process may have additional steps notincluded in the figure. Furthermore, examples of the methods may beimplemented by hardware, software, firmware, middleware, microcode,hardware description languages, or any combination thereof. Whenimplemented in software, firmware, middleware, or microcode, the programcode or code segments to perform the necessary tasks may be stored in anon-transitory computer-readable medium such as a storage medium.Processors may perform the described tasks.

Having described several example configurations, various modifications,alternative constructions, and equivalents may be used without departingfrom the spirit of the disclosure. For example, the above elements maybe components of a larger system, wherein other rules may takeprecedence over or otherwise modify the application of the invention.Also, a number of steps may be undertaken before, during, or after theabove elements are considered.

What is claimed is:
 1. A method for monitoring a resident, the methodcomprising: performing a confidence assessment based on a plurality ofsmart home devices being present within a residence linked with a useraccount, wherein the resident resides at the residence; determiningwhether the residence is eligible for monitoring of the resident basedon the confidence assessment; performing a learning process over aperiod of time during which resident activity data is collected from theplurality of smart home devices and analyzed to create an ordinarybehavior model; following determining that the residence is eligible formonitoring of the resident and the learning process being performed,providing a notification that monitoring is active; monitoring datareceived from the plurality of smart home devices to identify dataindicative of behavior considered unusual based on the ordinary behaviormodel; creating an alert that identifies the behavior and identifies howthe behavior contrasts with the ordinary behavior model; and sending, toan administrator device linked with the user account, the alert thatidentifies the behavior and identifies how the behavior contrasts withthe ordinary behavior model.
 2. The method for monitoring the residentof claim 1, wherein performing the confidence assessment comprisesidentifying a number of the plurality of smart home devices that areeligible to participate in the monitoring.
 3. The method for monitoringthe resident of claim 2, wherein performing the confidence assessmentfurther comprises identifying a second number of the plurality of smarthome devices that are eligible to participate in the monitoring and arepowered-constrained devices.
 4. The method for monitoring the residentof claim 3, further comprising: in response to the monitoring beingactivated, activating a process at each exclusively battery-poweredsmart home device of the plurality of smart home devices that definesone or more rules indicative of when data indicative of a behavior ofthe resident should be stored for periodic scheduled transmission to amonitoring server system or the data indicative of the behavior of theresident should be transmitted immediately to the monitoring serversystem.
 5. The method for monitoring the resident of claim 3, whereinperforming the confidence assessment further comprises providing aquestionnaire to the administrator device linked with the user account,wherein the questionnaire requires that a user of the administratordevice identify: a specific location of each smart home device of theplurality of smart home devices within the residence; and a plurality oftypes of worrisome scenarios of which the user of the administratordevice desires to be notified.
 6. The method for monitoring the residentof claim 5, wherein performing the confidence assessment furthercomprises providing the questionnaire to the administrator device linkedwith the user account, wherein the questionnaire requires that a user ofthe administrator device provide: an indication of a number of residentsthat live in the residence; and an indication that no cats or dogs livewith the resident.
 7. The method for monitoring the resident of claim 6,wherein performing the confidence assessment further comprises:calculating a confidence metric based on: the number of the plurality ofsmart home devices that are eligible to participate in the monitoring;the second number of the plurality of smart home devices that areeligible to participate in the monitoring and are powered constrained;and the responses to the questionnaire received from the administratordevice; and comparing the calculated confidence metric to a confidencemetric threshold.
 8. The method for monitoring the resident of claim 1,wherein the plurality of smart home devices are selected from the groupconsisting of: a smart home smoke detector; a smart home carbon monoxidedetector; a smart indoor security camera; a smart outdoor securitycamera; a smart thermostat; a smart home assistant device; a smartsecurity system; a smart window/door sensor; a smartphone; and a smartdoorbell device.
 9. The method for monitoring the resident of claim 1,wherein each of the plurality of smart home devices includes either avideo camera, microphone, or a motion sensor.
 10. The method formonitoring the resident of claim 1, further comprising: outputting, viaa smart home device of the plurality of smart home devices, the alert.11. The method for monitoring the resident of claim 1, whereinperforming the confidence assessment further comprises: monitoring theresident using the plurality of smart home devices over a trial periodof time to generate a body of trial monitoring data, the trial period oftime being sufficient to encompass at least a plurality of daily- and/orday-of-week-specific activity routines of the resident; processing thetrial monitoring data to determine whether the plurality of smart homedevices are sufficient in type, number, and location to sufficientlytrack the resident through their daily- and/or day-of-week-specificactivity routines according to a predetermined threshold criterion; anddetermining that the plurality of smart home devices are not sufficientin at least one of type, number, and location to sufficiently track theresident according to the predetermined threshold criterion, and,responsive to said determining, sending to the administrator device anotification that the residence is not eligible for said monitoring ofthe resident.
 12. The method for monitoring the resident of claim 11,wherein said predetermined threshold criterion includes that, for atleast a threshold percentage of each day of the trial period, a locationof the resident within the residence is identifiable solely using thetrial monitoring data.
 13. The method of claim 12, wherein saidthreshold percentage is at least 95 percent.
 14. The method of claim 12,wherein said predetermined threshold criterion includes that, for atleast the threshold percentage of each day of the trial period, abreathing rate or heartbeat rate of the resident within the residence isidentifiable solely using the trial monitoring data.
 15. The method ofclaim 11, wherein said trial period of time is a default value of atleast seven days, and wherein said performing the confidence assessmentis carried out without requiring user input.
 16. The method of claim 11,wherein said trial period of time is received from a user via theadministrative device.
 17. A system for monitoring a resident, thesystem comprising: a plurality of smart home devices installed within aresidence linked with a user account; an application executed by anadministrator device; and a cloud-based host system comprising: one ormore processors; and a memory communicatively coupled with and readableby the one or more processors and having stored thereinprocessor-readable instructions which, when executed by the one or moreprocessors, cause the one or more processors to: perform a confidenceassessment based on the plurality of smart home devices being presentwithin the residence linked with the user account, wherein the residentresides at the residence; determine whether the residence is eligiblefor monitoring of the resident based on the confidence assessment;perform a learning process over a period of time during which residentactivity data is collected from the plurality of smart home devices andanalyzed to create an ordinary behavior model; following determiningthat the residence is eligible for monitoring of the resident and thelearning process being performed, provide a notification that monitoringis active; monitor data received from the plurality of smart homedevices to identify data indicative of behavior considered unusual basedon the ordinary behavior model; create an alert that identifies thebehavior and identifies how the behavior contrasts with the ordinarybehavior model; and send, to the administrator device linked with theuser account, the alert that identifies the behavior and identifies howthe behavior contrasts with the ordinary behavior model.
 18. The systemfor monitoring the resident of claim 17, wherein the processor-readableinstructions that cause the one or more processors to perform theconfidence assessment further comprises processor-readable instructionsthat cause the one or more processors to identify a number of theplurality of smart home devices that are eligible to participate in themonitoring.
 19. The system for monitoring the resident of claim 18,wherein the processor-readable instructions that cause the one or moreprocessors to perform the confidence assessment further comprisesprocessor-readable instructions that cause the one or more processors toidentify a second number of the plurality of smart home devices that areeligible to participate in the monitoring and are powered-constraineddevices.
 20. The system for monitoring the resident of claim 19, whereinthe processor-readable instructions are further configured to cause theone or more processors to: in response to the monitoring beingactivated, activate a process at each power-constrained smart homedevice of the plurality of smart home devices that defines one or morerules indicative of when data indicative of a behavior of the residentshould be stored for periodic scheduled transmission to an monitoringserver system or the data indicative of the behavior of the residentshould be transmitted immediately to the monitoring server system. 21.The system for monitoring the resident of claim 19, wherein theprocessor-readable instructions that cause the one or more processors toperform the confidence assessment further comprises processor-readableinstructions that cause the one or more processors to provide aquestionnaire to the administrator device linked with the user account,wherein the questionnaire requires that a user of the administratordevice identify: a specific location of each smart home device of theplurality of smart home devices within the residence; and a plurality oftypes of worrisome scenarios of which the administrator user desires tobe notified.
 22. The system for monitoring the resident of claim 21,wherein the processor-readable instructions that cause the one or moreprocessors to perform the confidence assessment further comprisesprocessor-readable instructions that cause the one or more processors toprovide the questionnaire to the administrator device linked with theuser account, wherein the questionnaire requires that a user of theadministrator device provide: an indication of a number of residentsthat live in the residence; and an indication that no cats or dogs livewith the resident.
 23. The system for monitoring the resident of claim22, wherein the processor-readable instructions that cause the one ormore processors to perform the confidence assessment further comprisesprocessor-readable instructions that cause the one or more processorsto: calculating a confidence metric based on: the number of theplurality of smart home devices that are eligible to participate in themonitoring; the second number of the plurality of the smart home devicesthat are eligible to participate in the monitoring and are powerconstrained; and the responses to the questionnaire received from theadministrator device; and comparing the calculated confidence metric toa confidence metric threshold.
 24. The system for monitoring theresident of claim 17, wherein the plurality of smart home devices areselected from the group consisting of: a smart home smoke detector; asmart home carbon monoxide detector; a smart indoor security camera; asmart outdoor security camera; a smart thermostat; a smart homeassistant device; a smart security system; a smart window/door sensor; asmartphone; and a smart doorbell device.
 25. The system for monitoringthe resident of claim 17, wherein a smart home device of the pluralityof smart home devices, outputs the alert.
 26. A non-transitoryprocessor-readable medium comprising processor-readable instructionsconfigured to cause one or more processors to: perform a confidenceassessment based on a plurality of smart home devices being presentwithin a residence linked with a user account, wherein a residentresides at the residence; determine whether the residence is eligiblefor monitoring of the resident based on the confidence assessment;perform a learning process over a period of time during which residentactivity data is collected from the plurality of smart home devices andanalyzed to create an ordinary behavior model; following determiningthat the residence is eligible for monitoring of the resident and thelearning process being performed, provide a notification that monitoringis active; monitor data received from the plurality of smart homedevices to identify data indicative of behavior considered unusual basedon the ordinary behavior model; create an alert that identifies thebehavior and identifies how the behavior contrasts with the ordinarybehavior model; and cause to be sent, to an administrator device linkedwith the user account, the alert that identifies the behavior andidentifies how the behavior contrasts with the ordinary behavior model.